Spiking neural network connectivity and its potential for temporal sensory processing and variable binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well-understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modeling of neural circuits found in the brain.

Spiking Neural Network Connectivity and its Potential for Temporal Sensory Processing and Variable Binding

The most biologically-inspired artificial neurons are those of the third generation, and are termed spiking neurons, as individual pulses or spikes are the means by which stimuli are communicated. In essence, a spike is a short-term change in electrical potential and is the basis of communication between biological neurons. Unlike previous generations of artificial neurons, spiking neurons operate in the temporal domain, and exploit time as a resource in their computation. In 1952, Alan Lloyd Hodgkin and Andrew Huxley produced the first model of a spiking neuron; their model describes the complex electro-chemical process that enables spikes to propagate through, and hence be communicated by, spiking neurons. Since this time, improvements in experimental procedures in neurobiology, particularly with in vivo experiments, have provided an increasingly more complex understanding of biological neurons. For example, it is now well understood that the propagation of spikes between neurons requires neurotransmitter, which is typically of limited supply. When the supply is exhausted neurons become unresponsive. The morphology of neurons, number of receptor sites, amongst many other factors, means that neurons consume the supply of neurotransmitter at different rates. This in turn produces variations over time in the responsiveness of neurons, yielding various computational capabilities. Such improvements in the understanding of the biological neuron have culminated in a wide range of different neuron models, ranging from the computationally efficient to the biologically realistic. These models enable the modelling of neural circuits found in the brain. In recent years, much of the focus in neuron modelling has moved to the study of the connectivity of spiking neural networks. Spiking neural networks provide a vehicle to understand from a computational perspective, aspects of the brain’s neural circuitry. This understanding can then be used to tackle some of the historically intractable issues with artificial neurons, such as scalability and lack of variable binding. Current knowledge of feed-forward, lateral, and recurrent connectivity of spiking neurons, and the interplay between excitatory and inhibitory neurons is beginning to shed light on these issues, by improved understanding of the temporal processing capabilities and synchronous behaviour of biological neurons. This research topic aims to amalgamate current research aimed at tackling these phenomena.

Context-Based Sound and the Ecological Theory of Perception

This paper aims to investigate the ways in which context-based sonic art is capable of furthering a knowledge and understanding of place based on the initial perceptual encounter. How might this perceptual encounter operate in terms of a sound work’s affective dimension? To explore these issues I draw upon James J. Gibson’s ecological theory of perception and Gernot Böhme’s concept of an ‘aesthetic of atmospheres’. Within the ecological model of perception an individual can be regarded as a ‘perceptual system’: a mobile organism that seeks information from a coherent environment. I relate this concept to notions of the spatial address of environmental sound work in order to explore (a) how the human perceptual apparatus relates to the sonic environment in its mediated form and (b) how this impacts on individuals’ ability to experience such work as complex sonic ‘environments’. Can the ecological theory of perception aid the understanding of how the listener engages with context-based work? In proposing answers to this question, this paper advances a coherent analytical framework that may lead us to a more systematic grasp of the ways in which individuals engage aesthetically with sonic space and environment. I illustrate this methodology through an examination of some of the recorded work of sound artist Chris Watson.

Strength of visual percept generated by famous faces perceived without awareness: effects of affective valence, response latency, and visual field

Participants who were unable to detect familiarity from masked 17 ms faces ([Stone and Valentine, 2004] and [Stone and Valentine, in press-b]) did report a vague, partial visual percept. Two experiments investigated the relative strength of the visual percept generated by famous and unfamiliar faces, using masked 17 ms exposure. Each trial presented simultaneously a famous and an unfamiliar face, one face in LVF and the other in RVF. In one task, participants responded according to which of the faces generated the stronger visual percept, and in the other task, they attempted an explicit familiarity decision. The relative strength of the visual percept of the famous face compared to the unfamiliar face was moderated by response latency and participants’ attitude towards the famous person. There was also an interaction of visual field with response latency, suggesting that the right hemisphere can generate a visual percept differentiating famous from unfamiliar faces more rapidly than the left hemisphere. Participants were at chance in the explicit familiarity decision, confirming the absence of awareness of facial familiarity.

Beyond visual imagery: How modality-specific is enhanced mental imagery in synesthesia?

Synesthesia based in visual modalities has been associated with reports of vivid visual imagery. We extend this finding to consider whether other forms of synesthesia are also associated with enhanced imagery, and whether this enhancement reflects the modality of synesthesia. We used self‐report imagery measures across multiple sensory modalities, comparing synesthetes’ responses (with a variety of forms of synesthesia) to those of nonsynesthete matched controls. Synesthetes reported higher levels of visual, auditory, gustatory, olfactory and tactile imagery and a greater level of imagery use. Furthermore, their reported enhanced imagery is restricted to the modalities involved in the individual’s synesthesia. There was also a relationship between the number of forms of synesthesia an individual has, and the reported vividness of their imagery, highlighting the need for future research to consider the impact of multiple forms of synesthesia. We also recommend the use of behavioral measures to validate these self‐report findings.